DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to claims filed 03/30/2026 and Applicant’s request for reconsideration of application 17/383300 filed 03/30/2026.
Claims 1-19 have been examined with this office action.
Information Disclosure Statement
The information disclosure statement filed 03/30/2026 has been received, considered as indicated, and placed on record in the file.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of portfolio optimization without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claims 1 and 18 and all claims which depend from it are directed toward an apparatus, independent claim 19 and all claims which depend from it are directed toward a method, and independent claim 17 and all claims which depend from it are directed toward a computer readable medium storing instruction to perform functions/steps. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A machine learning portfolio generating apparatus, comprising: at least one memory; a component collection stored in the at least one memory;
any of at least one processor disposed in communication with the at least one memory, the any of at least one processor executing processor-executable instructions from the component collection, storage of the component collection structured with processor- executable instructions comprising:
obtain a portfolio construction request datastructure, the portfolio construction request datastructure structured including a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount; determine, via parameters from the portfolio construction request datastructure, a set of simulated market scenarios associated with the time period length, the set of simulated market scenarios generated with a set of deep learning neural historical market factor latent space networks, each simulated market scenario in the set of simulated market scenarios structured including a set of simulated market factor values; retrieve a set of expected returns for securities in the universe of securities for the set of simulated market scenarios, each expected return in the set of expected returns structured as calculated for a security during a simulated market scenario, in which the respective security's conditional Beta during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and the respective security's conditional default probability during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional default probability of the respective security, based on a second subset of the set of simulated market factor values; optimize portfolio weights of securities in the universe of securities in accordance with the conditional value at risk portion, the conditional value at risk threshold, and the portfolio value amount, with the set of expected returns, generating a set of tradeable transactions that maximize expected portfolio return of an optimized portfolio; and execute the set of tradeable transactions generating the optimized portfolio”.
Claim 17 comprises inter alia the functions or steps of “A machine learning portfolio generating processor-readable, non-transient medium, storage of the component collection structured with processor- executable instructions comprising: obtain, via any of at least one processor, a portfolio construction request datastructure, the portfolio construction request datastructure structured to include a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount; determine, via parameters from the portfolio construction request datastracutre, a set of simulated market scenarios associated with the time period length, the set of simulated market scenarios generated with a set of deep learning neural historical market factor latent space networks, each simulated market scenario in the set of simulated market scenarios structured including a set of simulated market factor values; retrieve a set of expected returns for securities in the universe of securities for the set of simulated market scenarios, each expected return in the set of expected returns structured as calculated for a security during a simulated market scenario, in which the respective security's conditional Beta during the respective simulated market scenario, determined with a set of decision tree ensembles, trained to estimate conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and the respective security's conditional default probability during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional default probability of the respective security, based on a second subset of the set of simulated market factor values; optimize portfolio weights of securities in the universe of securities in accordance with the conditional value at risk portion, the conditional value at risk threshold, and the portfolio value amount, with the set of expected returns, generating a set of tradeable transactions that maximize expected portfolio return of an optimized portfolio; and execute, via at least one processor, the set of tradeable transactions generating the optimized portfolio”.
Claim 18 comprises inter alia the functions or steps of “A machine learning portfolio generating processor-implemented system, comprising:means to store a component collection:
means to process processor-executable instructions from the component collection, storage of the component collection structured with processor-executable instructions including:
obtain, via any of at least one processor, a portfolio construction request datastructure, the portfolio construction request datastructure structured including a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount;determine parameters from the portfolio construction request datastructure, a set of simulated market scenarios associated with the time period length, the set of simulated market scenarios generated with a set of deep learning historical market factor latent space neural networks, each simulated market scenario in the set of simulated market scenarios structured including a set of simulated market factor values; retrieve a set of expected returns for securities in the universe of securities for the set of simulated market scenarios, each expected return in the set of expected returns structured as calculated for a security during a simulated market scenario, in which the respective security's conditional Beta during the respective simulated market scenario, determined with a set of decision tree ensembles, trained to estimate conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and the respective security's conditional default probability during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional default probability of the respective security, based on a second subset of the set of simulated market factor values; optimize portfolio weights of securities in the universe of securities in accordance with the conditional value at risk portion, the conditional value at risk threshold, and the portfolio value amount, with the set of expected returns, generating a set of tradeable transactions that maximize expected portfolio return of an optimized portfolio; and execute the set of tradeable transactions generating the optimized portfolio”.
Claim 19 comprises inter alia the functions or steps of “A machine learning portfolio generating processor-implemented process, including processing processor- executable instructions via any of at least one processor from a component collection stored in at least one memory, any of the component collection structured with processor- executable instructions comprising: obtain, via any of at least one processor, a portfolio construction request datastructure, the portfolio construction request datastructure structured including a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount;determine parameters from the portfolio construction request datastructure, a set of simulated market scenarios associated with the time period length, the set of simulated market scenarios generated with a set of deep learning historical market factor latent space neural networks, each simulated market scenario in the set of simulated market scenarios structured including a set of simulated market factor values; retrieve a set of expected returns for securities in the universe of securities for the set of simulated market scenarios, each expected return in the set of expected returns structured as calculated for a security during a simulated market scenario, in which the respective security's conditional Beta during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional Beta of the respective security, based on a first subset of the set of simulated market factor values, and
the respective security's conditional default probability during the respective simulated market scenario, determined with a set of decision tree ensembles, trained estimating conditional default probability of the respective security, based on a second subset of the set of simulated market factor values; optimize portfolio weights of securities in the universe of securities in accordance with the conditional value at risk portion, the conditional value at risk threshold, and the portfolio value amount, with the set of expected returns, generating a set of tradeable transactions that maximize expected portfolio return of an optimized portfolio; and execute the set of tradeable transactions generating the optimized portfolio”.
Those claim limits in bold are identified as claim limits directed toward the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Portfolio optimization is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way.
In particular, the claims contain the following additional elements: at least one memory; a component collection stored in in the at least one memory; any of at least one a processor; storage of the component collection structured; datastructure; deep learning neural networks; a set of decision tree ensembles, trained. However, the specification description of the additional elements at least one memory ([2589]); a component collection stored in in the at least one memory ([2603]); any of at least one a processor ([2601]); storage of the component collection structured ([2602]); datastructure ([0220]); deep learning neural networks ([0183]); a set of decision tree ensembles, trained ([0148-0155]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification as cited above for additional elements) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 2-16 these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
Prior Art
The claims overcome the prior art of record such that none of the cited prior art reference’s disclosures can be applied to form the basis of a 35 USC § 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC § 103 rejection when the limitations are read in the particular environment of the claims. Specifically, the closest prior art, Ding (PGPub No. 20220058739), Lange (PGPub No. 20170185922), Rebuth (PGPub No. 20220058738), and Li (PGPub No. 20160217366) do not show “the portfolio construction request datastructure structured including a set of optimization parameters including a universe of securities, a time period length, a conditional value at risk portion, a conditional value at risk threshold, a portfolio value amount” and/or the inputs/outputs of the deep neural network and decision tree ensembles. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Response to Arguments
Applicant's arguments with regards to claims have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but is not persuasive. Regarding applicant’s arguments directed toward Desjardins and additional resulting memos, the guidance provided based on the Desjardins decision did not change the basis of patent eligibility. Prong 2 considerations still involve determining if the claimed invention makes merely applies a technology environment or technology to an abstract idea as a tool or are indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field, or by applying or using the judicial exception in some other meaningful way. The examiner maintains that unlike Desjardins which claimed and disclosed an improvement to a technology, the presently claimed invention merely applies a technological environment to the abstract idea. The applicant has not identified, and examiner cannot find, disclosed or claimed improvement to a technology (i.e. memory; a processor; datastructure; deep learning neural networks; decision tree ensembles, training of data) within the specification or the claims. The additional elements perform their intended task and operate as tools for implementing the abstract idea of the claims.
The examiner has followed the MPEP which is the current analysis required for patent eligibility and the basis of the factual determination. As previously stated, the machine learning, deep learning neural network, training, and decision tree ensemble is claimed at a high level of generality and are merely applied to the abstract idea of the claims. There is no improvement to a computer or technology and does not apply the abstract idea to a particular machine. The examiner notes that a general purpose computer is flexible—it can do anything it is programmed to do. Therefore, the disclosure of a general purpose computer or a microprocessor as corresponding structure for a software function does nothing to limit the scope of the claim and “avoid pure functional claiming.” Further, the “prohibition against patenting abstract ideas ‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment’ or adding ‘insignificant postsolution activity.’” Bilski v. Kappos, 561 U.S. 593, 610–11 (2010) (quoting Diamond v. Diehr, 450 U.S. 175, 191–92 (1981)). Further, the claims do no apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. None of the claims limits have been ignored as applicant asserts. Instead, claim limits have been properly address as part of the abstract idea or as an additional element per patent eligibility guidance.
With regard to applicant's argument directed toward case law such as DDR Holdings, AMDOCS …, the cited applications made an improvement to an underlying technology, whereas, the present application used a generic technology and computer to implement an abstract idea. Thus, the cited case law is readily distinguishable from the present claims. The August 4, 2025 USPTO Memorandum on Subject Matter Eligibility does not change the basis of patent eligibility and the present analysis complies with the MPEP. As such, the examiner maintains the rejection.
Conclusion
For prior art made of record and not relied upon is considered pertinent to applicant's disclosure see Notice of References Cited items A-E submitted 06/03/2022 used as prior art and in the conclusion section in the office action submitted 06/03/2022.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached on 571 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Gregory A Pollock/Primary Examiner, Art Unit 3691
04/18/2026
Prosecution Insights